subject:"Re\: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2"

RE: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2

2016-07-21 Thread Ravi Aggarwal

Hi Ian,

Thanks for the information.

I think you are referring to post 
http://apache-spark-user-list.1001560.n3.nabble.com/How-spark-decides-whether-to-do-BroadcastHashJoin-or-SortMergeJoin-td27369.html.

Yeah I could solve above issue of mine using 
spark.sql.autoBroadcastJoinThreshold=-1, so that it always results in 
Sort-Merge join instead of BroadcastHashJoin, Rather ideal fix for me is to 
calculate size of my custom default source (BaseRelation’s sizeInBytes) in 
right manner, to make spark planner take appropriate decision for me.

Thanks
Ravi

From: ianoconn...@gmail.com [mailto:ianoconn...@gmail.com] On Behalf Of Ian 
O'Connell
Sent: Wednesday, July 20, 2016 11:05 PM
To: Ravi Aggarwal <raagg...@adobe.com>
Cc: Ted Yu <yuzhih...@gmail.com>; user <user@spark.apache.org>
Subject: Re: OutOfMemory when doing joins in spark 2.0 while same code runs 
fine in spark 1.5.2

Ravi did your issue ever get solved for this?

I think i've been hitting the same thing, it looks like the 
spark.sql.autoBroadcastJoinThreshold stuff isn't kicking in as expected, if I 
set that to -1 then the computation proceeds successfully.

On Tue, Jun 14, 2016 at 12:28 AM, Ravi Aggarwal 
<raagg...@adobe.com<mailto:raagg...@adobe.com>> wrote:
Hi,

Is there any breakthrough here?

I had one more observation while debugging the issue
Here are the 4 types of data I had:

Da -> stored in parquet
Di -> stored in parquet
Dl1 -> parquet version of lookup
Dl2 -> hbase version of lookup

Joins performed and type of join done by spark:
Da and Di Sort-merge failed (OOM)
Da and Dl1   B-H passed
Da and Dl2   Sort-Mergepassed
Di and Dl1B-H passed
Di and Dl2Sort-Mergefailed (OOM)

From entries I can deduce that problem is with sort-merge join involving Di.
So the hbase thing is out of equation, that is not the culprit.
In physical plan I could see there are only two operations that are done 
additionally in sort-merge as compared to Broadcast-hash.

==> Exchange Hashpartitioning

==> Sort
And finally sort-merge join.

Can we deduce anything from this?

Thanks
Ravi
From: Ravi Aggarwal
Sent: Friday, June 10, 2016 12:31 PM
To: 'Ted Yu' <yuzhih...@gmail.com<mailto:yuzhih...@gmail.com>>
Cc: user <user@spark.apache.org<mailto:user@spark.apache.org>>
Subject: RE: OutOfMemory when doing joins in spark 2.0 while same code runs 
fine in spark 1.5.2

Hi Ted,
Thanks for the reply.

Here is the code
Btw – df.count is running fine on dataframe generated from this default source. 
I think it is something in the combination of join and hbase data source that 
is creating issue. But not sure entirely.
I have also dumped the physical plans of both approaches s3a/s3a join and 
s3a/hbase join, In case you want that let me know.

import org.apache.hadoop.fs.FileStatus
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.{TableInputFormat}
import org.apache.hadoop.hbase._
import org.apache.hadoop.mapreduce.Job
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.CatalystTypeConverters
import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
import org.apache.spark.sql.execution.datasources.{OutputWriterFactory, 
FileFormat}
import org.apache.spark.sql.sources._
import org.apache.spark.sql.types._
import org.apache.spark.sql._
import org.slf4j.LoggerFactory

class DefaultSource extends SchemaRelationProvider with FileFormat {

  override def createRelation(sqlContext: SQLContext, parameters: Map[String, 
String], schema: StructType) = {
new HBaseRelation(schema, parameters)(sqlContext)
  }

  def inferSchema(sparkSession: SparkSession,
  options: Map[String, String],
  files: Seq[FileStatus]): Option[StructType] = ???

  def prepareWrite(sparkSession: SparkSession,
   job: Job,
   options: Map[String, String],
   dataSchema: StructType): OutputWriterFactory = ???
}

object HBaseConfigurationUtil {
  lazy val logger = LoggerFactory.getLogger("HBaseConfigurationUtil")
  val hbaseConfiguration = (tableName: String, hbaseQuorum: String) => {
val conf = HBaseConfiguration.create()
conf.set(TableInputFormat.INPUT_TABLE, tableName)
conf.set("hbase.mapred.outputtable", tableName)
conf.set("hbase.zookeeper.quorum", hbaseQuorum)
conf
  }
}

class HBaseRelation(val schema: StructType, parameters: Map[String, String])
   (@transient val sqlContext: SQLContext) extends BaseRelation 
with TableScan {

  import sqlContext.sparkContext

  override def buildScan(): RDD[Row] = {

val bcDataSchema = sparkContext.broadcast(schema)

val tableName = parameters.get("path") match

Re: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2

2016-07-20 Thread Ian O'Connell

Ravi did your issue ever get solved for this?

I think i've been hitting the same thing, it looks like
the spark.sql.autoBroadcastJoinThreshold stuff isn't kicking in as
expected, if I set that to -1 then the computation proceeds successfully.

On Tue, Jun 14, 2016 at 12:28 AM, Ravi Aggarwal <raagg...@adobe.com> wrote:

> Hi,
>
>
>
> Is there any breakthrough here?
>
>
>
> I had one more observation while debugging the issue
>
> Here are the 4 types of data I had:
>
>
>
> Da -> stored in parquet
>
> Di -> stored in parquet
>
> Dl1 -> parquet version of lookup
>
> Dl2 -> hbase version of lookup
>
>
>
> Joins performed and type of join done by spark:
>
> Da and Di Sort-merge failed (OOM)
>
> Da and Dl1   B-H passed
>
> Da and Dl2   Sort-Mergepassed
>
> Di and Dl1B-H passed
>
> Di and Dl2Sort-Mergefailed (OOM)
>
>
>
> From entries I can deduce that problem is with sort-merge join involving
> Di.
>
> So the hbase thing is out of equation, that is not the culprit.
>
> In physical plan I could see there are only two operations that are done
> additionally in sort-merge as compared to Broadcast-hash.
>
> è Exchange Hashpartitioning
>
> è Sort
>
> And finally sort-merge join.
>
>
>
> Can we deduce anything from this?
>
>
>
> Thanks
>
> Ravi
>
> *From:* Ravi Aggarwal
> *Sent:* Friday, June 10, 2016 12:31 PM
> *To:* 'Ted Yu' <yuzhih...@gmail.com>
> *Cc:* user <user@spark.apache.org>
> *Subject:* RE: OutOfMemory when doing joins in spark 2.0 while same code
> runs fine in spark 1.5.2
>
>
>
> Hi Ted,
>
> Thanks for the reply.
>
>
>
> Here is the code
>
> Btw – df.count is running fine on dataframe generated from this default
> source. I think it is something in the combination of join and hbase data
> source that is creating issue. But not sure entirely.
>
> I have also dumped the physical plans of both approaches s3a/s3a join and
> s3a/hbase join, In case you want that let me know.
>
>
>
> import org.apache.hadoop.fs.FileStatus
>
> import org.apache.hadoop.hbase.client._
>
> import org.apache.hadoop.hbase.io.ImmutableBytesWritable
>
> import org.apache.hadoop.hbase.mapreduce.{TableInputFormat}
>
> import org.apache.hadoop.hbase._
>
> import org.apache.hadoop.mapreduce.Job
>
> import org.apache.spark.rdd.RDD
>
> import org.apache.spark.sql.Row
>
> import org.apache.spark.sql.catalyst.CatalystTypeConverters
>
> import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
>
> import org.apache.spark.sql.execution.datasources.{OutputWriterFactory,
> FileFormat}
>
> import org.apache.spark.sql.sources._
>
> import org.apache.spark.sql.types._
>
> import org.apache.spark.sql._
>
> import org.slf4j.LoggerFactory
>
>
>
> class DefaultSource extends SchemaRelationProvider with FileFormat {
>
>
>
>   override def createRelation(sqlContext: SQLContext, parameters:
> Map[String, String], schema: StructType) = {
>
> new HBaseRelation(schema, parameters)(sqlContext)
>
>   }
>
>
>
>   def inferSchema(sparkSession: SparkSession,
>
>   options: Map[String, String],
>
>   files: Seq[FileStatus]): Option[StructType] = ???
>
>
>
>   def prepareWrite(sparkSession: SparkSession,
>
>job: Job,
>
>options: Map[String, String],
>
>dataSchema: StructType): OutputWriterFactory = ???
>
> }
>
>
>
> object HBaseConfigurationUtil {
>
>   lazy val logger = LoggerFactory.getLogger("HBaseConfigurationUtil")
>
>   val hbaseConfiguration = (tableName: String, hbaseQuorum: String) => {
>
> val conf = HBaseConfiguration.create()
>
> conf.set(TableInputFormat.INPUT_TABLE, tableName)
>
> conf.set("hbase.mapred.outputtable", tableName)
>
> conf.set("hbase.zookeeper.quorum", hbaseQuorum)
>
> conf
>
>   }
>
> }
>
>
>
> class HBaseRelation(val schema: StructType, parameters: Map[String,
> String])
>
>(@transient val sqlContext: SQLContext) extends
> BaseRelation with TableScan {
>
>
>
>   import sqlContext.sparkContext
>
>
>
>   override def buildScan(): RDD[Row] = {
>
>
>
> val bcDataSchema = sparkContext.broadcast(schema)
>
>
>
> val tableName = parameters.get("path") match {
>
>   case Some(t) => t
>
>

RE: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2

2016-06-14 Thread Ravi Aggarwal

Hi,

Is there any breakthrough here?

I had one more observation while debugging the issue
Here are the 4 types of data I had:

Da -> stored in parquet
Di -> stored in parquet
Dl1 -> parquet version of lookup
Dl2 -> hbase version of lookup

Joins performed and type of join done by spark:
Da and Di Sort-merge failed (OOM)
Da and Dl1   B-H passed
Da and Dl2   Sort-Mergepassed
Di and Dl1B-H passed
Di and Dl2Sort-Mergefailed (OOM)

From entries I can deduce that problem is with sort-merge join involving Di.
So the hbase thing is out of equation, that is not the culprit.
In physical plan I could see there are only two operations that are done 
additionally in sort-merge as compared to Broadcast-hash.

è Exchange Hashpartitioning

è Sort
And finally sort-merge join.

Can we deduce anything from this?

Thanks
Ravi
From: Ravi Aggarwal
Sent: Friday, June 10, 2016 12:31 PM
To: 'Ted Yu' <yuzhih...@gmail.com>
Cc: user <user@spark.apache.org>
Subject: RE: OutOfMemory when doing joins in spark 2.0 while same code runs 
fine in spark 1.5.2

Hi Ted,
Thanks for the reply.

Here is the code
Btw – df.count is running fine on dataframe generated from this default source. 
I think it is something in the combination of join and hbase data source that 
is creating issue. But not sure entirely.
I have also dumped the physical plans of both approaches s3a/s3a join and 
s3a/hbase join, In case you want that let me know.

import org.apache.hadoop.fs.FileStatus
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.{TableInputFormat}
import org.apache.hadoop.hbase._
import org.apache.hadoop.mapreduce.Job
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.CatalystTypeConverters
import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
import org.apache.spark.sql.execution.datasources.{OutputWriterFactory, 
FileFormat}
import org.apache.spark.sql.sources._
import org.apache.spark.sql.types._
import org.apache.spark.sql._
import org.slf4j.LoggerFactory

class DefaultSource extends SchemaRelationProvider with FileFormat {

  override def createRelation(sqlContext: SQLContext, parameters: Map[String, 
String], schema: StructType) = {
new HBaseRelation(schema, parameters)(sqlContext)
  }

  def inferSchema(sparkSession: SparkSession,
  options: Map[String, String],
  files: Seq[FileStatus]): Option[StructType] = ???

  def prepareWrite(sparkSession: SparkSession,
   job: Job,
   options: Map[String, String],
   dataSchema: StructType): OutputWriterFactory = ???
}

object HBaseConfigurationUtil {
  lazy val logger = LoggerFactory.getLogger("HBaseConfigurationUtil")
  val hbaseConfiguration = (tableName: String, hbaseQuorum: String) => {
val conf = HBaseConfiguration.create()
conf.set(TableInputFormat.INPUT_TABLE, tableName)
conf.set("hbase.mapred.outputtable", tableName)
conf.set("hbase.zookeeper.quorum", hbaseQuorum)
conf
  }
}

class HBaseRelation(val schema: StructType, parameters: Map[String, String])
   (@transient val sqlContext: SQLContext) extends BaseRelation 
with TableScan {

  import sqlContext.sparkContext

  override def buildScan(): RDD[Row] = {

val bcDataSchema = sparkContext.broadcast(schema)

val tableName = parameters.get("path") match {
  case Some(t) => t
  case _ => throw new RuntimeException("Table name (path) not provided in 
parameters")
}

val hbaseQuorum = parameters.get("hbaseQuorum") match {
  case Some(s: String) => s
  case _ => throw new RuntimeException("hbaseQuorum not provided in 
options")
}

val rdd = sparkContext.newAPIHadoopRDD(
  HBaseConfigurationUtil.hbaseConfiguration(tableName, hbaseQuorum),
  classOf[TableInputFormat],
  classOf[ImmutableBytesWritable],
  classOf[Result]
)

val rowRdd = rdd
  .map(tuple => tuple._2)
  .map { record =>

  val cells: java.util.List[Cell] = record.listCells()

  val splitRec = 
cells.toArray.foldLeft(Array(CellUtil.cloneRow(cells.get(0 {(a, b) =>
a :+ CellUtil.cloneValue(b.asInstanceOf[Cell])
  }

  val keyFieldName = bcDataSchema.value.fields.filter(e => 
e.metadata.contains("isPrimary") && e.metadata.getBoolean("isPrimary"))(0).name

  val schemaArr = cells.toArray.foldLeft(Array(keyFieldName)) {(a, b) => {
val fieldCell = b.asInstanceOf[Cell]
a :+ new 
String(fieldCell.getQualifierArray).substring(fieldCell.getQualifierOffset, 
fieldCell.getQualifierLength + fieldCell.getQualifierOffset)

RE: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2

2016-06-10 Thread Ravi Aggarwal

Hi Ted,
Thanks for the reply.

Here is the code
Btw – df.count is running fine on dataframe generated from this default source. 
I think it is something in the combination of join and hbase data source that 
is creating issue. But not sure entirely.
I have also dumped the physical plans of both approaches s3a/s3a join and 
s3a/hbase join, In case you want that let me know.

import org.apache.hadoop.fs.FileStatus
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.{TableInputFormat}
import org.apache.hadoop.hbase._
import org.apache.hadoop.mapreduce.Job
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.CatalystTypeConverters
import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
import org.apache.spark.sql.execution.datasources.{OutputWriterFactory, 
FileFormat}
import org.apache.spark.sql.sources._
import org.apache.spark.sql.types._
import org.apache.spark.sql._
import org.slf4j.LoggerFactory

class DefaultSource extends SchemaRelationProvider with FileFormat {

  override def createRelation(sqlContext: SQLContext, parameters: Map[String, 
String], schema: StructType) = {
new HBaseRelation(schema, parameters)(sqlContext)
  }

  def inferSchema(sparkSession: SparkSession,
  options: Map[String, String],
  files: Seq[FileStatus]): Option[StructType] = ???

  def prepareWrite(sparkSession: SparkSession,
   job: Job,
   options: Map[String, String],
   dataSchema: StructType): OutputWriterFactory = ???
}

object HBaseConfigurationUtil {
  lazy val logger = LoggerFactory.getLogger("HBaseConfigurationUtil")
  val hbaseConfiguration = (tableName: String, hbaseQuorum: String) => {
val conf = HBaseConfiguration.create()
conf.set(TableInputFormat.INPUT_TABLE, tableName)
conf.set("hbase.mapred.outputtable", tableName)
conf.set("hbase.zookeeper.quorum", hbaseQuorum)
conf
  }
}

class HBaseRelation(val schema: StructType, parameters: Map[String, String])
   (@transient val sqlContext: SQLContext) extends BaseRelation 
with TableScan {

  import sqlContext.sparkContext

  override def buildScan(): RDD[Row] = {

val bcDataSchema = sparkContext.broadcast(schema)

val tableName = parameters.get("path") match {
  case Some(t) => t
  case _ => throw new RuntimeException("Table name (path) not provided in 
parameters")
}

val hbaseQuorum = parameters.get("hbaseQuorum") match {
  case Some(s: String) => s
  case _ => throw new RuntimeException("hbaseQuorum not provided in 
options")
}

val rdd = sparkContext.newAPIHadoopRDD(
  HBaseConfigurationUtil.hbaseConfiguration(tableName, hbaseQuorum),
  classOf[TableInputFormat],
  classOf[ImmutableBytesWritable],
  classOf[Result]
)

val rowRdd = rdd
  .map(tuple => tuple._2)
  .map { record =>

  val cells: java.util.List[Cell] = record.listCells()

  val splitRec = 
cells.toArray.foldLeft(Array(CellUtil.cloneRow(cells.get(0 {(a, b) =>
a :+ CellUtil.cloneValue(b.asInstanceOf[Cell])
  }

  val keyFieldName = bcDataSchema.value.fields.filter(e => 
e.metadata.contains("isPrimary") && e.metadata.getBoolean("isPrimary"))(0).name

  val schemaArr = cells.toArray.foldLeft(Array(keyFieldName)) {(a, b) => {
val fieldCell = b.asInstanceOf[Cell]
a :+ new 
String(fieldCell.getQualifierArray).substring(fieldCell.getQualifierOffset, 
fieldCell.getQualifierLength + fieldCell.getQualifierOffset)
  }
  }

  val res = Map(schemaArr.zip(splitRec).toArray: _*)

  val recordFields = res.map(value => {
val colDataType =
  try {
bcDataSchema.value.fields.filter(_.name == value._1)(0).dataType
  } catch {
case e: ArrayIndexOutOfBoundsException => throw new 
RuntimeException("Schema doesn't contain the fieldname")
  }
CatalystTypeConverters.convertToScala(
  Cast(Literal(value._2), colDataType).eval(),
  colDataType)
  }).toArray
  Row(recordFields: _*)
}

rowRdd
  }
}

Thanks
Ravi

From: Ted Yu [mailto:yuzhih...@gmail.com]
Sent: Thursday, June 9, 2016 7:56 PM
To: Ravi Aggarwal <raagg...@adobe.com>
Cc: user <user@spark.apache.org>
Subject: Re: OutOfMemory when doing joins in spark 2.0 while same code runs 
fine in spark 1.5.2

bq. Read data from hbase using custom DefaultSource (implemented using 
TableScan)

Did you use the DefaultSource from hbase-spark module in hbase master branch ?
If you wrote your own, mind sharing related code ?

Thanks

On Thu, Jun 9, 2016 at 2:53 AM, raaggarw 
<raagg...@adobe.com<mailto:raagg...@adobe.com>> wrote:
Hi,

I was

Re: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2

2016-06-09 Thread Ted Yu

bq. Read data from hbase using custom DefaultSource (implemented using
TableScan)

Did you use the DefaultSource from hbase-spark module in hbase master
branch ?
If you wrote your own, mind sharing related code ?

Thanks

On Thu, Jun 9, 2016 at 2:53 AM, raaggarw  wrote:

> Hi,
>
> I was trying to port my code from spark 1.5.2 to spark 2.0 however i faced
> some outofMemory issues. On drilling down i could see that OOM is because
> of
> join, because removing join fixes the issue. I then created a small
> spark-app to reproduce this:
>
> (48 cores, 300gb ram - divided among 4 workers)
>
> line1 :df1 = Read a set a of parquet files into dataframe
> line2: df1.count
> line3: df2 = Read data from hbase using custom DefaultSource (implemented
> using TableScan)
> line4: df2.count
> line5: df3 = df1.join(df2, df1("field1") === df2("field2"), "inner")
> line6: df3.count -> *this is where it fails in Spark 2.0 and runs fine in
> spark 1.5.2*
>
> Problem:
> First lot of WARN messages
> 2016-06-09 08:14:18,884 WARN  [broadcast-exchange-0]
> memory.TaskMemoryManager (TaskMemoryManager.java:allocatePage(264)) -
> Failed
> to allocate a page (1048576 bytes), try again.
> And then OOM
>
> I then tried to dump data fetched from hbase into s3 and then created df2
> from s3 rather than hbase, then it worked fine in spark 2.0 as well.
>
> Could someone please guide me through next steps?
>
> Thanks
> Ravi
> Computer Scientist @ Adobe
>
>
>
>
> --
> View this message in context:
> http://apache-spark-user-list.1001560.n3.nabble.com/OutOfMemory-when-doing-joins-in-spark-2-0-while-same-code-runs-fine-in-spark-1-5-2-tp27124.html
> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>
> -
> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
> For additional commands, e-mail: user-h...@spark.apache.org
>
>

RE: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2

Re: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2

RE: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2

RE: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2

Re: OutOfMemory when doing joins in spark 2.0 while same code runs fine in spark 1.5.2

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